import mne

bids_root = "mergedDataset"
deriv_root = "mergedDataset/derivatives"
subjects_dir = None
#subjects = ["NDARAB678VYW","NDARDC504KWE","NDARDL033XRG","NDARTK720LER","NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARDZ794ZVP"] #"all" #["NDARAG429CGW"]
#subjects = ["NDARAB678VYW"] ##["NDARKM301DY0"] #"all"
#subjects = ["NDARAF535XK6"]
#subjects = ["NDARUC804LKP","NDARVD609JNZ","NDARGN483WFH","NDARFY623ZTE","NDARFN221WW5","NDARXR865BVX","NDARRK528GFZ","NDARKP823HEM","NDARYA857NDW","NDARUR298LVX","NDARWT403LP6","NDARDL033XRG","NDARYJ413BLN","NDARLP413TUX","NDARLM981MEN","NDARKG016KD1","NDARAG788YV9","NDARUA035YJN","NDARNP381RZ4","NDARZG044CJ5","NDARHT518WEM","NDARDX544FJ0","NDARKM199DXW","NDARWC905XUG","NDARYH501UH3","NDARRK146XCZ"]
subjects = ["NDARKT714TXR","NDARUC804LKP","NDARGN483WFH","NDARAL897CYV","NDARTA920XFC","NDARPW746FWF","NDARDL033XRG","NDARRK528GFZ","NDARBT436PMT","NDARZK745JGG"]

ch_types = ["eeg"]
interactive = False
sessions = []#"all"
task = "symbolSearch"
#task_et = "WISC_ProcSpeed"

task_is_rest = True
rest_epochs_duration = 5
rest_epochs_overlap = 0
runs = ["1"]
et_has_run = False
et_has_task = True

epochs_tmin = 0
#rest_epochs_duration = 10
#rest_epochs_overlap = 0
baseline = None
#baseline: tuple[float | None, float | None] | None = (-0.2, 0)

#raw_resample_sfreq: float | None = 250

eeg_reference = "average"

ica_l_freq = 1 # ?

# determined by icalabel
l_freq: float | None = 1
h_freq: float | None = 100
ica_h_freq: float | None = 100

# data was recorded in the US
notch_freq = 60

on_error = "continue"


######### Remove these when doing Unfold analysis! ############

# positive / negative feedback
#conditions = ["HAPPY", "SAD"]
##conditions = ["Fixation L"]
##
##epochs_tmin: float = -0.5
##epochs_tmax: float = 2.6 # since feedback is so infrequent, long epochs are okay
##
##baseline: tuple[float | None, float | None] | None = (-0.2, 0)
###############################################################



spatial_filter = "ica"
# ica_n_components = 96 ?
ica_algorithm = "picard-extended_infomax"
#ica_use_ecg_detection: bool = True
#ica_use_eog_detection: bool = True
ica_use_icalabel = True
#ica_reject: dict[str, float] | Literal["autoreject_local"] | None = "autoreject_local"

ica_reject = "autoreject_local" #TESTING
reject = "autoreject_local" #TESTING

#These are identical, just ensuring compatibility
sync_eyelink = True
sync_eye = True

#sync_eventtype_regex = "\\d-trigger=10 Image moves"
#sync_eventtype_regex_et = "trigger=10 Image moves"

#Contrast detection
#sync_eventtype_regex     = r"contrastTrial_start"
#sync_eventtype_regex_et  = r"# Message: 15"

sync_eventtype_regex     = r"(?:trialResponse|newPage)" #r"trialResponse"
sync_eventtype_regex_et  = r"# Message: (?:14|20)" #r"# Message: 14"

#sync_eventtype_regex     = r"trialResponse"
#sync_eventtype_regex_et  = r"# Message: 14"


#eog_channels = ["HEOGL", "HEOGR", "VEOGL", "VEOGU"]

#eeg_bipolar_channels = {"HEOG": ("HEOGL", "HEOGR"), "VEOG": ("VEOGL", "VEOGU")}
#eog_channels = ["HEOG", "VEOG"]
#sync_heog_ch = ("HEOG")


#eeg_bipolar_channels = {"HEOG": ("E40", "E109"),
#                            "VEOG": ("E21",  "E127")} #left eye
##eeg_bipolar_channels = {
##    #"HEOG": ("E127", "E126"),
##    "HEOG": ("E126", "E127"),
##    "VEOG": ("E22", "E127"), #left eye
##}


eeg_bipolar_channels = {
    #"HEOG": ("E127", "E126"),

    # Version 1, doesn't work well
    #### "HEOG": ("E127", "E126"), 
    #### "VEOG": ("E22", "E127"), #left eye

    # Version 2, works well but not sure why
    ###"HEOG": ("E109", "E40"),
    ###"VEOG": ("E22", "E127"), 

    # Version 3, seems to work well?
    "HEOG": ("E2", "E26"),
    "VEOG": ("E3", "E8")
}


eog_channels = ["HEOG", "VEOG"]

sync_heog_ch = "HEOG"

sync_et_ch = ("L POR X [px]", "R POR X [px]")

#sync_et_ch = "xpos_right"
sync_plot_samps = 3000

decode: bool = False
run_source_estimation = False

montage = mne.channels.make_standard_montage("GSN-HydroCel-128")

eeg_template_montage = montage
drop_channels = ["Cz"]

n_jobs = 1

  Platform             Linux-5.14.0-427.96.1.el9_4.x86_64-x86_64-with-glibc2.34
Python               3.11.7 (main, Aug 29 2025, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-4)]
Executable           /pfs/work9/workspace/scratch/st_st156392-mydata/mnevenv/bin/python
CPU                  Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz (128 cores)
Memory               251.5 GiB

Core
├☑ mne               1.10.2 (latest release)
├☑ numpy             2.3.4 (OpenBLAS 0.3.30 with 1 thread)
├☑ scipy             1.16.2
└☑ matplotlib        3.10.7 (backend=agg)

Numerical (optional)
├☑ sklearn           1.7.2
├☑ nibabel           5.3.2
├☑ pandas            2.3.3
├☑ h5io              0.2.5
├☑ h5py              3.15.1
└☐ unavailable       numba, nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.46.3 (OpenGL 4.5 (Compatibility Profile) Mesa 23.3.3 via llvmpipe (LLVM 17.0.6, 256 bits))
├☑ pyvistaqt         0.11.3
├☑ vtk               9.5.2
└☐ unavailable       qtpy, ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.17.0
├☑ mne-icalabel      0.8.1
├☑ mne-bids-pipeline 0.1.0.dev917+g2366e2b9a
├☑ eeglabio          0.1.2
├☑ edfio             0.4.10
├☑ pybv              0.7.6
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, neo, mffpy